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Parsimonious Network Based On A Fuzzy Inference System (Panfis) For Time Series Feature Prediction Of Low Speed Slew Bearing Prognosis, Wahyu Caesarendra, Mahardhika Pratama, Buyung Kosasih, Tegoeh Tjahjowidodo, Adam Glowacz

Faculty of Engineering and Information Sciences - Papers: Part B

In recent years, the utilization of rotating parts, e.g., bearings and gears, has been continuously supporting the manufacturing line to produce a consistent output quality. Due to their critical role, the breakdown of these components might significantly impact the production rate. Prognosis, which is an approach that predicts the machine failure, has attracted significant interest in the last few decades. In this paper, the prognostic approaches are described briefly and advanced predictive analytics, namely a parsimonious network based on a fuzzy inference system (PANFIS), is proposed and tested for low speed slew bearing data. PANFIS differs itself from conventional prognostic …